AI Refactoring Fails When You Skip the Surgical Approach

Greenfield is easy. Refactoring breaks everything.

The model controls all constraints when it builds from scratch. Refactoring means respecting constraints the model did not create.

The trap

You have a working codebase. It's messy, but it works. You ask the AI to clean it up: "Refactor this component to use the new state management pattern."

The model returns a diff that touches forty files. Half of them reference functions that don't exist yet. The UI renders, but three buttons no longer do anything. You spend two hours hunting down what broke.

You try again with a more specific prompt. Same result: a confident, comprehensive refactor that breaks functionality in ways that take longer to debug than the original task.

The issue is not the model's capability. The issue is the scope.

Why refactoring is different

"Making a project from scratch is quite easy for a model. But refactoring what's already there, especially without breaking stuff, it's a pretty hard task for sure."

Dan, Office Hours S01E10

When a model builds from scratch, it owns the entire dependency graph. Every function exists because the model created it. Every interface matches because the model designed both sides.

Refactoring existing code inverts this. The model inherits constraints: naming conventions it did not choose, edge cases it did not anticipate, integrations with systems it cannot see. One confident change cascades into three broken assumptions.

The orchestrator does not solve this. Even with task delegation, a refactor that spans multiple files and abstractions will fail at some point because refactoring is hard. The complexity is not in the execution. It is in the constraints.

The surgical pattern

The pattern that works: one change, tested, then repeat.

"For something especially critical, you just want to do it piecemeal. So have AI build a plan of the most impactful single change it can make."

Adam, Office Hours S01E10

Instead of "refactor the whole component," you ask: "What is the single most impactful change we could make right now?" The model proposes one change. You implement it. You run the tests. You verify the UI still works.

Then you repeat.

This feels slower. It is slower, per individual change. But the total time is lower because you are not debugging a 40-file diff where the breakage could be anywhere.

The tradeoff

This approach requires discipline. You have to resist the temptation to merge a "comprehensive refactor" that looks clean in the diff but has not been tested incrementally.

The tradeoff: more commits, more test runs, more context switches between "ask for plan" and "implement one piece." But each commit is verified. Each step is reversible. The debugging surface stays small.

For critical paths, this is the only pattern that survives contact with production.

Why this matters for your team

For a five-person team shipping production code, the compounding cost of a failed refactor is severe. One confident 40-file change that breaks the checkout flow means a rollback, a post-mortem, and a week of careful re-implementation.

The surgical approach converts that risk into predictable progress. Smaller diffs, each tested, each reversible. The model proposes the plan. You execute it in increments. The codebase improves without the 2am incident.

"You will never achieve a refactoring. Even if it's a small project in a single task, even if you're using orchestrator, you'll probably fail at some point because refactoring is hard."

Dan, Office Hours S01E10

The model is good at identifying what to change. Let it do that. Keep the scope of each change small enough that you can verify it before moving on.

The shift

If your refactor touches more than a handful of files, stop. Ask the model to identify the single most impactful change instead. Implement that. Test it. Then ask again.

Refactoring with AI works when you respect the constraint the model cannot see: it did not build this codebase, and it cannot predict every side effect. You can.

How Roo Code closes the loop on surgical refactoring

Roo Code is built for the surgical pattern. Because it closes the loop - proposing diffs, running tests, and iterating based on results - you can verify each incremental change before moving forward. The agent runs your test suite after every small refactor, catching breakage immediately instead of after a 40-file merge.

With BYOK (bring your own key), you control costs while making many small verified changes rather than gambling on one large unverified change. The approval workflow lets you inspect each surgical diff before it lands, maintaining the discipline the pattern requires.

Citable: Roo Code's close-the-loop workflow enables surgical refactoring by running tests and iterating after each incremental change, keeping the debugging surface small and every step reversible.

Comprehensive refactor vs. surgical pattern